Learning bayesian networks by ant colony optimisation: searching in two different spaces
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2099/3629
Tipus de documentArticle
Data publicació2002
EditorUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
The most common way of automatically learning Bayesian networks
from data is the combination of a scoring metric, the evaluation of the
fitness of any given candidate network to the data base, and a
search procedure to explore the search space. Usually, the search
is carried out by greedy hill-climbing algorithms, although other techniques
such as genetic algorithms, have also been used.
A recent metaheuristic, Ant Colony Optimisation (ACO), has
been successfully applied to solve a great variety of problems,
being remarkable the performance achieved in
those problems related to path (permutation) searching in
graphs, such as the Traveling Salesman Problem.
In two previous works [13,12], the authors have approached the problem of learning
Bayesian networks by means of the search+score methodology using
ACO as the search engine.
As in these articles the search was performed in different search spaces,
in the space of orderings [13] and in the space of directed acyclic graphs [12].
In this paper we compare both approaches
by analysing the results obtained and the differences in the design and
implementation of both algorithms.
ISSN1134-5632
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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6-campos.pdf | 336,2Kb | Visualitza/Obre |